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Soil total nitrogen (STN) is a crucial component of the ecosystem's nitrogen pool, and accurate prediction of STN content is essential for understanding global nitrogen cycling processes. This study utilized the measured STN content of 126 sample points and 40 extracted remote sensing variables to predict the STN content and map its spatial distribution in the northeastern coastal region of Hebei Province, China, employing the random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) methods. The purpose was to compare the ability of remote sensing images (Landsat-8, Sentinel-1, and Sentinel-2) with different machine learning methods for predicting STN content. The research results show the following: (1) The three machine learning methods accurately predicted the STN content and the optimal model provided by the XGBoost method, with an R2 of 0.627, RMSE of 0.127 g·kg−1, and MAE of 0.092 g·kg−1. (2) The combination of optical and synthetic aperture radar (SAR) images improved prediction accuracy, with the R2 improving by 45.5%. (3) The importance of optical images is higher than that of SAR images in the RF, GBM, and XGBoost methods, with optical images accounting for 87%, 76%, and 77% importance, respectively. (4) The spatial distribution of STN content predicted by the three methods is similar. Higher STN contents are distributed in the northern part of the study area, while lower STN contents are distributed in coastal areas. The results of this study can be very useful for inventories of soil nitrogen and provide data support and method references for revealing nitrogen cycling. [ABSTRACT FROM AUTHOR]

Asymmetric triangulation is an interesting method combined with concentric pleating to obtain a 3D shape without stretching or tearing. There exists some geometric properties in the process of folding to help realize extension and contraction, which can be used in parametric modeling of different regular polygons. To facilitate design and modeling, adequate computational modeling methods are indispensable. This paper proposes a new mathematical idea and presents a feasible way to build the parameterized models in the digital environment of Rhinoceros, utilizing the Kangaroo plugin in Grasshopper. Designers can directly observe the models' kinematic deployment and calculate the folding efficiency. It is concluded that the tendencies of folding efficiency in different regular polygons are not the same. To realize rigid folding, each polygon has a limited folding angle. [ABSTRACT FROM AUTHOR]

Coastal wetland soil organic carbon (CW-SOC) is crucial for wetland ecosystem conservation and carbon cycling. The accurate prediction of CW-SOC content is significant for soil carbon sequestration. This study, which employed three machine learning (ML) methods, including random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost), aimed to estimate CW-SOC content using 98 soil samples, SAR images, optical images, and climate and topographic data. Three statistical metrics and leave-one-out cross-validation were used to evaluate model performance. Optimal models using different ML methods were applied to predict the spatial distribution of CW-SOC content. The results showed the following: (1) The models built using optical images had higher predictive accuracy than models built using synthetic aperture radar (SAR) images. The model that combined SAR images, optical images, and climate data demonstrated the highest prediction accuracy. Compared to the model using only optical images and SAR images, the prediction accuracy was improved by 0.063 and 0.115, respectively. (2) Regardless of the combination of predictive variables, the XGBoost method achieved higher prediction accuracy than the RF and GBM methods. (3) Optical images were the main explanatory variables for predicting CW-SOC content, explaining more than 65% of the variability. (4) The CW-SOC content predicted by the three ML methods showed similar spatial distribution characteristics. The central part of the study area had higher CW-SOC content, while the southern and northern regions had lower levels. This study accurately predicted the spatial distribution of CW-SOC content, providing data support for ecological environmental protection and carbon neutrality of coastal wetlands. [ABSTRACT FROM AUTHOR]

The pathogenesis of type 2 diabetes (T2D) is highly related to the abnormal self-assembly of the human islet amyloid polypeptide (hIAPP) into amyloid aggregates. To inhibit hIAPP aggregation is considered a promising therapeutic strategy for T2D treatment. Melatonin (Mel) was reported to effectively impede the accumulation of hIAPP aggregates and dissolve preformed fibrils. However, the underlying mechanism at the atomic level remains elusive. Here, we performed replica-exchange molecular dynamics (REMD) simulations to investigate the inhibitory effect of Mel on hIAPP oligomerization by using hIAPP20–29 octamer as templates. The conformational ensemble shows that Mel molecules can significantly prevent the β-sheet and backbone hydrogen bond formation of hIAPP20–29 octamer and remodel hIAPP oligomers and transform them into less compact conformations with more disordered contents. The interaction analysis shows that the binding behavior of Mel is dominated by hydrogen bonding with a peptide backbone and strengthened by aromatic stacking and CH–π interactions with peptide sidechains. The strong hIAPP–Mel interaction disrupts the hIAPP20–29 association, which is supposed to inhibit amyloid aggregation and cytotoxicity. We also performed conventional MD simulations to investigate the influence and binding affinity of Mel on the preformed hIAPP1–37 fibrillar octamer. Mel was found to preferentially bind to the amyloidogenic region hIAPP20–29, whereas it has a slight influence on the structural stability of the preformed fibrils. Our findings illustrate a possible pathway by which Mel alleviates diabetes symptoms from the perspective of Mel inhibiting amyloid deposits. This work reveals the inhibitory mechanism of Mel against hIAPP20–29 oligomerization, which provides useful clues for the development of efficient anti-amyloid agents. [ABSTRACT FROM AUTHOR]

The pathology Alzheimer's disease (AD) is associated with the self-assembly of amyloid-β (Aβ) peptides into β-sheet enriched fibrillar aggregates. A promising treatment strategy is focused on the inhibition of amyloid fibrillization of Aβ peptide. Fullerene C60 is proved to effectively inhibit Aβ fibrillation while the poor water-solubility restricts its use as a biomedicine agent. In this work, we examined the interaction of fullerene C60 and water-soluble fullerenol C60(OH)6/C60(OH)12 (C60 carrying 6/12 hydroxyl groups) with preformed Aβ40/42 protofibrils by multiple molecular dynamics simulations. We found that when binding to the Aβ42 protofibril, C60, C60(OH)6 and C60(OH)12 exhibit distinct binding dynamics, binding sites and peptide interaction. The increased number of hydroxyl groups C60 carries leads to slower binding dynamics and weaker binding strength. Binding free energy analysis demonstrates that the C60/C60(OH)6 molecule primarily binds to the C-terminal residues 31–41, whereas C60(OH)12 favors to bind to N-terminal residues 4–14. The hydrophobic interaction plays a critical role in the interplay between Aβ and all the three nanoparticles, and the π-stacking interaction gets weakened as C60 carries more hydroxyls. In addition, the C60(OH)6 molecule has high affinity to form hydrogen bonds with protein backbones. The binding behaviors of C60/C60(OH)6/C60(OH)12 to the Aβ40 protofibril resemble with those to Aβ42. Our work provides a detailed picture of fullerene/fullerenols binding to Aβ protofibril, and is helpful to understand the underlying inhibitory mechanism. [ABSTRACT FROM AUTHOR]

Inflammation is the body's self-protective response to multiple stimulus, from external harmful substances to internal danger signals released after trauma or cell dysfunction. Many diseases are considered to be related to inflammation, such as cancer, metabolic disorders, aging, and neurodegenerative diseases. Current therapeutic approaches include mainly non-steroidal anti-inflammatory drugs and glucocorticoids, which are generally of limited effectiveness and severe side-effects. Thus, it is urgent to develop novel effective anti-inflammatory therapeutic agents. Xanthones, a unique scaffold with a 9H-Xanthen-9-one core structure, widely exist in natural sources. Till now, over 250 xanthones were isolated and identified in plants from the families Gentianaceae and Hypericaceae. Many xanthones have been disclosed with anti-inflammatory properties on different models, either in vitro or in vivo. Herein, we provide a comprehensive and up-to-date review of xanthones with anti-inflammatory properties, and analyzed their drug likeness, which might be potential therapeutic agents to fight against inflammation-related diseases. [ABSTRACT FROM AUTHOR]